Not logged in
PANGAEA.
Data Publisher for Earth & Environmental Science

Chu, Kai; Winter, Christian; Hebbeln, Dierk; Schulz, Michael (2013): Medem Channel profiles based on stand-alone model simulation and corrections with nudging. PANGAEA, https://doi.org/10.1594/PANGAEA.815843, Supplement to: Chu, K et al. (2013): Improvement of morphodynamic modeling of tidal channel migration by nudging. Coastal Engineering, 77, 1-13, https://doi.org/10.1016/j.coastaleng.2013.02.004

Always quote citation above when using data! You can download the citation in several formats below.

RIS CitationBibTeX Citation

Abstract:
State-of-the-art process-based models have shown to be applicable to the simulation and prediction of coastal morphodynamics. On annual to decadal temporal scales, these models may show limitations in reproducing complex natural morphological evolution patterns, such as the movement of bars and tidal channels, e.g. the observed decadal migration of the Medem Channel in the Elbe Estuary, German Bight. Here a morphodynamic model is shown to simulate the hydrodynamics and sediment budgets of the domain to some extent, but fails to adequately reproduce the pronounced channel migration, due to the insufficient implementation of bank erosion processes. In order to allow for long-term simulations of the domain, a nudging method has been introduced to update the model-predicted bathymetries with observations. The model-predicted bathymetry is nudged towards true states in annual time steps. Sensitivity analysis of a user-defined correlation length scale, for the definition of the background error covariance matrix during the nudging procedure, suggests that the optimal error correlation length is similar to the grid cell size, here 80-90 m. Additionally, spatially heterogeneous correlation lengths produce more realistic channel depths than do spatially homogeneous correlation lengths. Consecutive application of the nudging method compensates for the (stand-alone) model prediction errors and corrects the channel migration pattern, with a Brier skill score of 0.78. The proposed nudging method in this study serves as an analytical approach to update model predictions towards a predefined 'true' state for the spatiotemporal interpolation of incomplete morphological data in long-term simulations.
Parameter(s):
#NameShort NameUnitPrincipal InvestigatorMethod/DeviceComment
1DistanceDistancemChu, Kai
2Depth, reconstructedReconstr depthmChu, KaiModelling system Delft3D and nudgingof 1993
3Depth, reconstructedReconstr depthmChu, KaiModelling system Delft3D and nudgingof 1995
4Depth, reconstructedReconstr depthmChu, KaiModelling system Delft3D and nudgingof 1996
5Depth, reconstructedReconstr depthmChu, KaiModelling system Delft3D and nudgingof 1997
6Depth, reconstructedReconstr depthmChu, KaiModelling system Delft3D and nudgingof 1998
7Depth, reconstructedReconstr depthmChu, KaiModelling system Delft3D and nudgingof 1999
8Depth, reconstructedReconstr depthmChu, KaiModelling system Delft3D and nudgingof 2000
9Depth, reconstructedReconstr depthmChu, KaiModelling system Delft3D and nudgingof 2003
10Depth, reconstructedReconstr depthmChu, KaiModelling system Delft3D and nudgingof 2004
11Depth, reconstructedReconstr depthmChu, KaiModelling system Delft3D and nudgingof 2005
12Depth, reconstructedReconstr depthmChu, KaiModelling system Delft3D and nudgingof 2007
Size:
492 data points

Download Data

Download dataset as tab-delimited text — use the following character encoding:

View dataset as HTML